Which storage solution in the Hadoop ecosystem is designed for handling small files and is used as a complementary storage layer alongside HDFS? ________
- HBase
- Hadoop Archives (HAR)
- Hive
- Kudu
Kudu is a storage solution in the Hadoop ecosystem specifically designed for handling small files efficiently. It serves as a complementary storage layer alongside Hadoop Distributed File System (HDFS) and is optimized for workloads involving random access to data, such as time-series data or small analytical queries.
Scenario: You are tasked with designing a real-time analytics application using Apache Flink. Which feature of Apache Flink would you utilize for exactly-once processing semantics?
- Checkpointing
- Savepoints
- State TTL (Time-To-Live)
- Watermarking
Checkpointing in Apache Flink is the feature used for ensuring exactly-once processing semantics. Checkpoints capture the state of the application at regular intervals, allowing Flink to recover from failures and guaranteeing that each record is processed exactly once, even in the presence of failures or restarts.
Which of the following is NOT an authentication factor?
- Something you are
- Something you have
- Something you know
- Something you need
The concept of authentication factors revolves around verifying the identity of a user before granting access to resources. "Something you need" does not align with the typical authentication factors. The correct factors are: something you know (like a password), something you have (like a security token or smart card), and something you are (biometric identifiers such as fingerprints or facial recognition).
________ is a principle of data protection that requires organizations to limit access to sensitive data only to authorized users.
- Data anonymization
- Data confidentiality
- Data minimization
- Data segregation
The correct answer is Data confidentiality. Data confidentiality is a fundamental principle of data protection that emphasizes restricting access to sensitive information to authorized users only. It involves implementing security measures such as encryption, access controls, and authentication mechanisms to safeguard data from unauthorized access, disclosure, or alteration. By maintaining data confidentiality, organizations can protect sensitive information from unauthorized disclosure, data breaches, and privacy violations, thereby preserving trust and compliance with regulatory requirements.
What role does data profiling play in the data extraction phase of a data pipeline?
- Encrypting sensitive data
- Identifying patterns, anomalies, and data quality issues
- Loading data into the target system
- Transforming data into a standardized format
Data profiling in the data extraction phase involves analyzing the structure and quality of the data to identify patterns, anomalies, and issues, which helps in making informed decisions during the data pipeline process.
What is the significance of consistency in data quality metrics?
- It ensures that data is uniform and coherent across different sources and applications
- It focuses on the timeliness of data updates
- It measures the completeness of data within a dataset
- It validates the accuracy of data through manual verification
Consistency in data quality metrics refers to the uniformity and coherence of data across various sources, systems, and applications. It ensures that data elements have the same meaning and format wherever they are used, reducing the risk of discrepancies and errors in data analysis and reporting. Consistent data facilitates interoperability, data integration, and reliable decision-making processes within organizations.
________ is a common technique used in monitoring data pipelines to identify patterns indicative of potential failures.
- Anomaly detection
- Data encryption
- Data masking
- Data replication
Anomaly detection is a prevalent technique used in monitoring data pipelines to identify unusual patterns or deviations from expected behavior. By analyzing metrics such as throughput, latency, error rates, and data quality, anomaly detection algorithms can flag potential issues such as system failures, data corruption, or performance degradation, allowing data engineers to take proactive measures to mitigate them.
________ is a data extraction technique that involves querying data from web pages and web APIs.
- Data Wrangling
- ETL (Extract, Transform, Load)
- Streaming
- Web Scraping
Web Scraping is a data extraction technique that involves querying data from web pages and web APIs. It allows for automated retrieval of data from various online sources for further processing and analysis.
How do data modeling tools like ERWin or Visio support reverse engineering in the context of existing databases?
- Data lineage tracking, Data migration, Data validation, Data cleansing
- Data profiling, Data masking, Data transformation, Data visualization
- Importing database schemas, Generating entity-relationship diagrams, Metadata extraction, Schema synchronization
- Schema comparison, Code generation, Query execution, Database optimization
Data modeling tools like ERWin or Visio support reverse engineering by enabling tasks such as importing existing database schemas, generating entity-relationship diagrams, extracting metadata, and synchronizing the schema with changes made in the tool.
The ETL process often involves loading data into a ________ for further analysis.
- Data Lake
- Data Mart
- Data Warehouse
- None of the above
In the ETL process, data is frequently loaded into a Data Warehouse, a central repository where it can be organized, integrated, and analyzed for business insights.
In a relational database, a join that returns all rows from both tables, joining records where available and inserting NULL values for missing matches, is called a(n) ________ join.
- Cross join
- Inner join
- Left join
- Outer join
An outer join in a relational database returns all rows from both tables, joining records where available and inserting NULL values for missing matches. This includes both left and right outer joins.
How does Apache Flink handle event time processing?
- Implements sequential processing
- Relies on batch processing techniques
- Uses synchronized clocks for event ordering
- Utilizes watermarks and windowing
Apache Flink handles event time processing by utilizing watermarks and windowing techniques. Watermarks are markers that signify the progress of event time within the stream and are used to trigger computations based on the completeness of the data. Windowing enables the grouping of events into time-based or count-based windows for aggregation and analysis. By combining watermarks and windowing, Flink ensures accurate and efficient event time processing, even in the presence of out-of-order events or delayed data arrival.